In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import os
import re
from imp import reload
import h5py
import sys
In [3]:
import deltascope as cranium
import deltascope.alignment as ut
In [4]:
at = ".\\data\\24hpf\\AT\\Prob"
gfap = ".\\data\\24hpf\\Gfap\\Prob"
In [5]:
outdir = ".\\data\\24hpf\\Output-02-15-2019"
In [6]:
os.mkdir(outdir)
In [7]:
Dat = {}
for f in os.listdir(at):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dat[num] = os.path.join(at,f)
In [8]:
Dzrf = {}
for f in os.listdir(gfap):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dzrf[num] = os.path.join(gfap,f)
In [9]:
Dat.keys()
Out[9]:
dict_keys(['10', '11', '12', '17', '19', '1', '21', '23', '24', '25', '26', '27', '28', '3', '4', '5', '6', '7', '8', '9'])
In [10]:
Dbat = {}
Dbzrf={}

Data Preprocessing

In [11]:
klist = Dat.keys()
In [12]:
param = {
    'gthresh':0.5,
    'scale':[1,1,1],
    'microns':[0.16,0.16,0.21],
    'mthresh':0.8,
    'radius':10,
    'comp_order':[0,2,1],
    'fit_dim':['x','z'],
    'deg':2
}
In [13]:
for k in klist:
    try:
        Dbat[k] = ut.preprocess(Dat[k],param)
        Dbzrf[k] = ut.preprocess(Dzrf[k],param,pca=Dbat[k].pcamed,mm=Dbat[k].mm,vertex=Dbat[k].vertex)
        print(k)
    except:
        print(k,'failed')
        e = sys.exc_info()
        print(e)

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

10

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

11

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

12

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

17 failed
(<class 'ValueError'>, ValueError('Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required.'), <traceback object at 0x000002072ED89048>)

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

19

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

1

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

21

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

23

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

24 failed
(<class 'ValueError'>, ValueError('Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required.'), <traceback object at 0x000002072EDB2B08>)

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

25

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

26

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

27

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

28

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

3

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

4

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

5

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

6

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

7

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

8

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

9 failed
(<class 'ValueError'>, ValueError('Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required.'), <traceback object at 0x000002072F1CA448>)
In [14]:
def start(k):
    return(ut.start(k,Dbat,[Dbzrf],im=False))
def save_at(k,df):
    ut.save_at(k,df,outdir,'24hpf')
def save_both(k,dfa,dfb):
    ut.save_both(k,dfa,dfb,outdir,'24hpf')
In [15]:
def fit_model(axi,df,mm=None):
    if mm == None:
        mm = np.polyfit(df.x,df.z,2)
    p = np.poly1d(mm)
    xrange = np.arange(np.min(df.x),np.max(df.x))
    axi.plot(xrange,p(xrange),c='m')
    return(mm)
In [24]:
model = pd.DataFrame({'a':[],'b':[],'c':[]})
def save_model(k,mm,model):
    row = pd.Series({'a':mm[0],'b':mm[1],'c':mm[2]},name=k)
    model = model.append(row)
    return(model)
In [35]:
def pick_pts(x1,z1,vx,vz,x2,z2):
    pts = pd.DataFrame({'x':[x1,vx,x2],'z':[z1,vz,z2]})
    return(pts)
In [25]:
klist
Out[25]:
dict_keys(['10', '11', '12', '17', '19', '1', '21', '23', '24', '25', '26', '27', '28', '3', '4', '5', '6', '7', '8', '9'])

7

In [26]:
k,df,Ldf,ax = start('7')
In [27]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [28]:
pts.iloc[1].x = 36
pts.iloc[1].z = 22
ax[0,1].scatter(pts.x,pts.z,c='y')
Out[28]:
<matplotlib.collections.PathCollection at 0x20758c1dcc0>
In [29]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [30]:
df3,Ldf3,p,ax = ut.check_yz(df2,Ldf2)
In [31]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3)
In [36]:
pts = pick_pts(-42,27,0,-4,40,27)
ax = ut.make_graph([df4]+Ldf4)
ax[0,1].scatter(pts.x,pts.z,c='m',s=40)
Out[36]:
<matplotlib.collections.PathCollection at 0x20758a5cf60>
In [37]:
df5,Ldf5,mm,ax = ut.ch_vertex(df4,Ldf4,pts=pts)
In [38]:
save_both(k,df5,Ldf5[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_7_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_7_24hpf.psi complete

26

In [39]:
k,df,Ldf,ax = start('26')
In [40]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [41]:
pts.iloc[1].x = 18
pts.iloc[1].z = 2
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[41]:
x z
0 -52.087549 41.197433
1 18.000000 2.000000
In [42]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [43]:
df3,Ldf3,pts,ax = ut.check_pts(df2,Ldf2,'y')
In [44]:
pts.iloc[1].x = 16
pts.iloc[1].y = -1
pts.iloc[0].y = 18
ax[0,0].scatter(pts.x,pts.y,c='y')
pts
Out[44]:
x y
0 -65.223682 18.0
1 16.000000 -1.0
In [45]:
df4,Ldf4,ax = ut.revise_pts(df2,Ldf2,'y',pts=pts)
In [46]:
df5,Ldf5,p,ax = ut.check_yz(df4,Ldf4)
In [47]:
p2 = np.poly1d([2/5,0])
xrange = np.arange(-20,20)
p[0,2].plot(xrange,p2(xrange),c='c')
Out[47]:
[<matplotlib.lines.Line2D at 0x20758522668>]
In [48]:
df6,Ldf6,ax,p = ut.check_yz(df4,Ldf4,mm=[2/5,0])
In [49]:
df7,Ldf7,mm,ax = ut.ch_vertex(df6,Ldf6)
In [50]:
pts = pick_pts(-40,20,0,-8,40,20)
ax[0,1].scatter(pts.x,pts.z,c='m',s=40)
Out[50]:
<matplotlib.collections.PathCollection at 0x20806cba940>
In [51]:
df8,Ldf8,mm,ax = ut.ch_vertex(df7,Ldf7,pts=pts)
In [52]:
save_both(k,df8,Ldf8[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_26_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_26_24hpf.psi complete
In [53]:
model = model.iloc[-2:]
In [54]:
model
Out[54]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00

9

In [56]:
Dbat['9'] = ut.preprocess(os.path.join(at,'AT_24hpf_9_Probabilities.h5'),param,stop='df_thresh')
Dbzrf['9'] = ut.preprocess(os.path.join(gfap,'Gfap_24hpf_9_Probabilities.h5'),param,stop='df_thresh')
In [57]:
k = '9'
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
In [58]:
ax = ut.make_graph([df]+Ldf)
In [59]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf)
In [60]:
save_both(k,df1,Ldf1[0])
model = save_model(k,mm,model)
model

Write to .\data\24hpf\Output-02-15-2019\AT_9_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_9_24hpf.psi complete
Out[60]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00
9 0.004727 2.061185e-16 6.484843e-15

5

In [61]:
k,df,Ldf,ax = start('5')
In [62]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [63]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [64]:
pts = pick_pts(-55,22,0,-8,58,20)
ax[0,1].scatter(pts.x,pts.z,s=50,c='m')
Out[64]:
<matplotlib.collections.PathCollection at 0x20806e3c4a8>
In [65]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [66]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)
model

Write to .\data\24hpf\Output-02-15-2019\AT_5_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_5_24hpf.psi complete
Out[66]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00
9 0.004727 2.061185e-16 6.484843e-15
5 0.009099 5.287413e-17 -1.423254e-14

1

In [67]:
k = '1'
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
In [68]:
ax = ut.make_graph([df]+Ldf)
In [69]:
df1,Ldf1,ax,p = ut.check_yz(df,Ldf)
In [70]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [71]:
pts = pick_pts(-55,20,-6,-20,35,20)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[71]:
<matplotlib.collections.PathCollection at 0x2072fcb6e10>
In [72]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [73]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)
model

Write to .\data\24hpf\Output-02-15-2019\AT_1_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_1_24hpf.psi complete
Out[73]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00
9 0.004727 2.061185e-16 6.484843e-15
5 0.009099 5.287413e-17 -1.423254e-14
1 0.019910 -4.560282e-16 1.231414e-14

21

In [74]:
k,df,Ldf,ax = start('21')
In [75]:
df1,Ldf1,ax,p = ut.check_yz(df,Ldf)
In [76]:
mm = [3/5,0]
p = np.poly1d(mm)
xrange = np.arange(-10,30)
ax[0,2].plot(xrange,p(xrange),c='y')
Out[76]:
[<matplotlib.lines.Line2D at 0x208084a22e8>]
In [77]:
df2,Ldf2,ax,p = ut.check_yz(df,Ldf,mm=mm)
In [78]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [79]:
pts = pick_pts(-48,24,0,-6,45,20)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[79]:
<matplotlib.collections.PathCollection at 0x20758d0c940>
In [80]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [81]:
save_both(k,df4,Ldf4[0])
model = save_model(k,mm,model)
model

Write to .\data\24hpf\Output-02-15-2019\AT_21_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_21_24hpf.psi complete
Out[81]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00
9 0.004727 2.061185e-16 6.484843e-15
5 0.009099 5.287413e-17 -1.423254e-14
1 0.019910 -4.560282e-16 1.231414e-14
21 0.012933 -1.773224e-16 -8.838063e-16

12

In [82]:
k,df,Ldf,ax = start('12')
In [83]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [84]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [85]:
pts = pick_pts(-30,10,5,-8,35,10)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[85]:
<matplotlib.collections.PathCollection at 0x20809078f98>
In [86]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [87]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_12_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_12_24hpf.psi complete

24

In [89]:
Dbat['24'] = ut.preprocess(os.path.join(at,'AT_24hpf_24_Probabilities.h5'),param,stop='df_thresh')
Dbzrf['24'] = ut.preprocess(os.path.join(gfap,'Gfap_24hpf_24_Probabilities.h5'),param,stop='df_thresh')
In [90]:
k = '24'
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [91]:
df1,Ldf1,p,ax = ut.check_yz(df,Ldf)
In [92]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [93]:
save_both(k,df2,Ldf2[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_24_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_24_24hpf.psi complete

28

In [94]:
k = '28'
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [95]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'y')
In [96]:
pts.iloc[0].x = 60
pts.iloc[0].y = 17
ax[0,0].scatter(pts.x,pts.y,c='c')
pts
Out[96]:
x y
0 60.00 17.00
1 6.72 8.64
In [97]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'y',pts=pts)
In [98]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [99]:
pts = pick_pts(-28,9,0,-3,23,9)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[99]:
<matplotlib.collections.PathCollection at 0x208636768d0>
In [100]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [101]:
save_both(k,df4,Ldf4[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_28_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_28_24hpf.psi complete

3

In [102]:
k,df,Ldf,ax = start('3')
In [103]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [104]:
pts.iloc[1].z = 7
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[104]:
x z
0 58.142891 42.86977
1 -34.095611 7.00000
In [105]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [106]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [107]:
pts = pick_pts(-50,22,0,-10,47,20)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[107]:
<matplotlib.collections.PathCollection at 0x20897388dd8>
In [108]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [109]:
save_both(k,df4,Ldf4[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_3_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_3_24hpf.psi complete

10

In [110]:
k,df,Ldf,ax = start('10')
In [111]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [112]:
pts.iloc[1].x = -28
pts.iloc[1].z = 10
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[112]:
x z
0 46.552267 55.507086
1 -28.000000 10.000000
In [113]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [114]:
pts = pick_pts(-58,25,0,-2,45,25)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[114]:
<matplotlib.collections.PathCollection at 0x208632bf160>
In [115]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [116]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_10_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_10_24hpf.psi complete

27

In [117]:
k,df,Ldf,ax = start('27')
In [118]:
df1,Ldf1 = ut.zyswitch(df,Ldf)
In [119]:
ax = ut.make_graph([df1]+Ldf1)
In [120]:
df2,Ldf2,pts,ax = ut.check_pts(df1,Ldf1,'y')
In [121]:
pts
pts.iloc[0].x = -55
pts.iloc[0].y = 10
pts.iloc[1].x = 30
pts.iloc[1].y = 5
ax[0,0].scatter(pts.x,pts.y,c='c')
Out[121]:
<matplotlib.collections.PathCollection at 0x20806ebb780>
In [122]:
df3,Ldf3,ax = ut.revise_pts(df1,Ldf1,'y',pts=pts)
In [123]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3)
In [124]:
pts = pick_pts(-40,9,0,-7,40,11)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[124]:
<matplotlib.collections.PathCollection at 0x20897665e48>
In [125]:
df5,Ldf5,mm,ax = ut.ch_vertex(df4,Ldf4,pts=pts)
In [126]:
save_both(k,df5,Ldf5[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_27_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_27_24hpf.psi complete

17

In [128]:
k = '17'
Dbat[k] = ut.preprocess(os.path.join(at,'AT_24hpf_17_Probabilities.h5'),param,stop='df_thresh')
Dbzrf[k] = ut.preprocess(os.path.join(gfap,'Gfap_24hpf_17_Probabilities.h5'),param,stop='df_thresh')
In [129]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [130]:
df1,Ldf1,p,ax = ut.check_yz(df,Ldf)
In [131]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [132]:
pts = pick_pts(-38,16,0,-8,45,16)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[132]:
<matplotlib.collections.PathCollection at 0x208974277b8>
In [133]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [134]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_17_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_17_24hpf.psi complete

4

In [135]:
k,df,Ldf,ax = start('4')
In [136]:
ut.make_graph([df,Dbat[k].df_thresh])
Out[136]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002089818B438>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000020898447128>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002089846E550>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000020898495AC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000208984C8080>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000208984ED5C0>]],
      dtype=object)
In [137]:
df1,Ldf1,mm,ax = ut.ch_vertex(Dbat[k].df_thresh,[Dbzrf[k].df_thresh])
In [138]:
pts = pick_pts(-55,6,0,-5,52,6)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[138]:
<matplotlib.collections.PathCollection at 0x208980d6a20>
In [139]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1,pts=pts)
In [140]:
save_both(k,df2,Ldf2[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_4_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_4_24hpf.psi complete

11

In [141]:
k,df,Ldf,ax = start('11')
In [142]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf)
In [143]:
pts = pick_pts(-48,11,-5,-8,36,8)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[143]:
<matplotlib.collections.PathCollection at 0x20863127c18>
In [144]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1,pts=pts)
In [145]:
save_both(k,df2,Ldf2[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_11_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_11_24hpf.psi complete

6

In [146]:
k,df,Ldf,ax = start('6')
In [147]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [148]:
pts.iloc[1].x = -56
pts.iloc[1].z = 24
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[148]:
x z
0 44.665085 36.879888
1 -56.000000 24.000000
In [149]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [150]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [151]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_6_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_6_24hpf.psi complete

23

In [153]:
k = '23'
Dbat[k] = ut.preprocess(os.path.join(at,'AT_24hpf_23_Probabilities.h5'),param,stop='df_thresh')
Dbzrf[k] = ut.preprocess(os.path.join(gfap,'Gfap_24hpf_23_Probabilities.h5'),param,stop='df_thresh')
In [154]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [155]:
df1,Ldf1,p,ax = ut.check_yz(df,Ldf)
In [156]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [157]:
save_both(k,df2,Ldf2[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_23_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_23_24hpf.psi complete

19

In [158]:
k,df,Ldf,ax = start('19')
In [159]:
pts = pick_pts(-65,20,0,-10,55,17)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[159]:
<matplotlib.collections.PathCollection at 0x208d11e05f8>
In [160]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [161]:
save_both(k,df1,Ldf1[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_19_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_19_24hpf.psi complete

25

In [162]:
k,df,Ldf,ax = start('25')
In [163]:
df1,Ldf1,ax,p = ut.check_yz(df,Ldf)
In [164]:
p
Out[164]:
poly1d([ 0.40313491, -2.74690804])
In [165]:
df2,Ldf2,ax,p = ut.check_yz(df,Ldf,mm=[1,-2.7])
In [166]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [167]:
save_both(k,df3,Ldf3[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_25_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_25_24hpf.psi complete

8

In [168]:
k,df,Ldf,ax = start('8')
In [169]:
df1,Ldf1,ax,p = ut.check_yz(df,Ldf)
In [170]:
df2,Ldf2,pts,ax = ut.check_pts(df1,Ldf1,'z')
In [171]:
pts.iloc[1].x = 43
pts.iloc[1].z = 25
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[171]:
x z
0 -54.177133 31.661079
1 43.000000 25.000000
In [172]:
df3,Ldf3,ax = ut.revise_pts(df1,Ldf1,'z',pts=pts)
In [173]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3)
In [174]:
save_both(k,df4,Ldf4[0])
model = save_model(k,mm,model)

Write to .\data\24hpf\Output-02-15-2019\AT_8_24hpf.psi complete
Write to .\data\24hpf\Output-02-15-2019\ZRF_8_24hpf.psi complete
In [175]:
model
Out[175]:
a b c
7 0.018452 1.914462e-17 -5.692721e-21
26 0.017500 2.761276e-17 0.000000e+00
9 0.004727 2.061185e-16 6.484843e-15
5 0.009099 5.287413e-17 -1.423254e-14
1 0.019910 -4.560282e-16 1.231414e-14
21 0.012933 -1.773224e-16 -8.838063e-16
12 0.017143 2.387306e-16 -5.129661e-15
24 0.011212 1.409065e-16 3.431518e-15
28 0.018634 -2.287230e-16 3.079476e-15
3 0.013178 -9.440544e-17 5.942779e-15
10 0.010345 2.094698e-16 -2.061921e-15
27 0.010625 -1.097348e-16 1.418498e-15
17 0.014035 -6.426921e-17 6.152684e-15
4 0.003846 3.740995e-17 -2.563972e-15
11 0.009906 7.740906e-17 2.494437e-15
6 0.008516 1.994954e-16 -3.326923e-15
23 0.010611 -4.715987e-16 -2.876130e-14
19 0.007937 -1.523041e-16 -6.897951e-15
25 0.003600 4.781800e-18 3.397189e-15
8 0.007573 -8.906381e-18 3.158467e-15
In [176]:
model.to_csv(os.path.join(outdir,'model.csv'))